Search Results (1,751)

Psychiatric disorders affect nearly one billion people worldwide and remain a major therapeutic challenge due to frequent treatment resistance. Pharmacogenetics provides a precision-informed approach by accounting for interindividual variability in drug metabolism and response, and population-specific data offer valuable information for therapeutic considerations. This study analyzed 3011 Greek individuals to assess 24 pharmacogenetic variants across 13 genes. Genotyping was performed using TaqMan OpenArray^® assays, and metabolic phenotypes were predicted based on established genotype-to-phenotype translation guidelines. Allele frequencies were compared with those in European, African, and East Asian populations. Population structure and genetic differentiation were evaluated using Principal Component Analysis (PCA), K-means clustering, fixation index (F_ST), and STRUCTURE analysis. Results indicated that most allele frequencies in Greeks aligned with those in European populations, while several CYP2D6 and CYP2C19 variants differed significantly from those in African and East Asian cohorts. PCA and clustering confirmed strong European affinity, supported by low F_ST values, whereas STRUCTURE revealed minimal non-European admixture. Predicted metabolic phenotypes showed that 36%, 57.7%, and 41.6% of individuals exhibited altered CYP2D6, CYP2C19, and CYP2C9 activity, respectively. These findings highlight clinically actionable variation in the Greek population and emphasize the use of population-specific pharmacogenetic data to inform optimized strategies in precision psychiatry. Full article

Looking into early Christian–Muslim relations seems to be the outcome of greater interest in Islam transcultural encounters due to current issues of mass migration. Sicily presents an informative example of the interaction between different ethnic and religious groups over centuries. Several scholars, including Jeremy Johns, Alex Metcalfe and Julie Taylor, have explored the social and administrative position of Christians and Muslims within the complex society of Sicily, although their contributions were largely from the umbrella of Norman Sicily from the eleventh to the thirteenth centuries. Thus, there is a need to shift away from the Normans’ experience to exploring Christian–Muslim relations in Sicily during the ninth through eleventh centuries, especially the expansion, society and activities during the rule of the Fāṭimids of Ifrīqiya (909–965) and their Kalbid allies (948–1053). These forms of relationships are not only important for Sicily but for the whole region of the central Mediterranean. This paper will build on the works of Umberto Rizzitano and other scholars to explore the relations between the Arabs and Muslims and the Christians in Sicily during the Muslim rule of the Island. Using Arabic and Islamic sources, including travel accounts by the Muslim geographer Ibn Ḥawqal (d. 988), this paper aims to discuss the lives of Christians and their dynamic exchanges with Muslims within the social and political complexities of Aghlabid and Fāṭimid Sicily as well as Sicily’s link to North Africa (Ifrīqiya). Sicily’s proximity to North Africa and to Europe has been an essential aspect of its history, which facilitated movement of communities between these regions. The paper will also compare the policies of the Fāṭimids towards Christians in Sicily with their relations towards their Christian subjects in Cairo, Egypt. It will show the pragmatic aspects of this relationship concerning marriage, legal status, the movement of people, and cultural and intellectual exchange. Christians and Muslims practised cultural hybridisation that brought changes in Sicily with respect to language, religion, and social habits, resulting in a distinctive Sicilian multicultural identity. Full article

Quantum walks are, at present, an active field of study in mathematics, with important applications in quantum information and statistical physics. In this paper, we determine the influence of basic chaotic features on the walker behavior. For this purpose, we consider an extremely simple model consisting of alternating one-dimensional walks along the two spatial coordinates in bidimensional closed domains (hard wall billiards). The chaotic or regular behavior induced by the boundary shape in the deterministic classical motion translates into chaotic signatures for the quantized problem, resulting in sharp differences in the spectral statistics and morphology of the eigenfunctions of the quantum walker. Indeed, we found, for the Bunimovich stadium—a chaotic billiard—level statistics described by a Brody distribution with parameter $\delta \simeq 0.1$. This indicates a weak level repulsion, and also enhanced eigenfunction localization, with an average participation ratio $(\mathrm{PR})\simeq 1150$ compared to the rectangular billiard (regular) case, where the average $\mathrm{PR}\simeq 1500$. Furthermore, scarring on unstable periodic orbits is observed. The fact that our simple model exhibits such key signatures of quantum chaos, e.g., non-Poissonian level statistics and scarring, that are sensitive to the underlying classical dynamics in the free particle billiard system is utterly surprising, especially when taking into account that quantum walks are diffusive models, which are not direct quantizations of a Hamiltonian. Full article

We present a quantitative theory of contraction and expansion cycles within the Quantum Memory Matrix (QMM) cosmology. In this framework, spacetime consists of finite-capacity Hilbert cells that store quantum information. Each non-singular bounce adds a fixed increment of imprint entropy, defined as the cumulative quantum information written irreversibly into the matrix and distinct from coarse-grained thermodynamic entropy, thereby providing an intrinsic, monotonic cycle counter. By calibrating the geometry–information duality, inferring today’s cumulative imprint from CMB, BAO, chronometer, and large-scale-structure constraints, and integrating the modified Friedmann equations with imprint back-reaction, we find that the Universe has already completed $N_{\mathrm{past}}=3.6\pm 0.4$ cycles. The finite Hilbert capacity enforces an absolute ceiling: propagating the holographic write rate and accounting for instability channels implies only $N_{\mathrm{future}}=7.8\pm 1.6$ additional cycles before saturation halts further bounces. Integrating Kodama-vector proper time across all completed cycles yields a total cumulative age $t_{\mathrm{QMM}}=62.0\pm 2.5\mathrm{Gyr}$, compared to the $13.8\pm 0.2\mathrm{Gyr}$ of the current expansion usually described by $\mathrm{\Lambda }$CDM. The framework makes concrete, testable predictions: an enhanced faint-end UV luminosity function at $z\gtrsim 12$ observable with JWST, a stochastic gravitational-wave background with $f^{2/3}$ scaling in the LISA band from primordial black-hole mergers, and a nanohertz background with slope $\alpha \simeq 2/3$ accessible to pulsar-timing arrays. These signatures provide near-term opportunities to confirm, refine, or falsify the cyclical QMM chronology. Full article

As native bovine whey (WHEY) poses environmental concerns as a high-water-content by-product, this trial aimed at assessing the effectiveness of a thermal–mechanical microparticulation coupled with a fermentative process to concentrate it into a high-protein soft dairy cream. Compared to native whey, in microparticulated (MPW) and fermented (FMPW) matrices, there was a significant increase in proteins (from 0.7 to 8.8%) and lipids (from 0.3 to 1.3%), and a more brilliant yellowness colour. A factorial discriminant analysis (FDA) showed that FMPW had a higher content of saturated fatty acid (SFA) and some specific polyunsaturated fatty acid (PUFA) n-6, and also identified C14:0, C18:1, C18:1 t-11, C18:2 n-6, and C18:3 n-6 as informative biomarkers of microparticulation and fermentative treatments. The SDS-PAGE indicated no effects on the protein profile but indicated its rearrangement into high molecular weight aggregates. Z-sizer and transmission electron microscopy analyses confirmed a different supramolecular structure corresponding to a higher variability and greater incidence of very large molecular aggregates, suggesting that MPW could be accounted as a colloidal matrix that may have similar ball-bearing lubrication properties. Microparticulation of whey could facilitate its circularity into the dairy supply chain through its re-generation from a waste into a high-value fat replacer for dairy-based food production. Full article

The methylation of CpG sites with 5mC mark is a dynamic epigenetic modification. However, the relationship between the methylation and the surrounding genomic sequence context remains poorly explored. Investigation of the allele methylation provides an opportunity to decipher the interplay between differences in the primary DNA sequence and epigenetic variation. Here, we performed high-coverage long-read whole-genome direct DNA sequencing of one individual using Oxford Nanopore technology. We also used Illumina whole-genome sequencing of the parental genomes in order to identify allele-specific methylation sites with a trio-binning approach. We have compared the results of the haplotype-specific methylation detection and revealed that trio binning outperformed other approaches that do not take into account parental information. Also, we analysed the cis-regulatory effects of the genomic variations for influence on CpG methylation. To this end, we have used available Deep Learning models trained on the primary DNA sequence to score the cis-regulatory potential of the genomic loci. We evaluated the functional role of the allele-specific epigenetic changes with respect to gene expression using long-read Nanopore RNA sequencing. Our analysis revealed that the frequency of SNVs near allele-specific methylation positions is approximately four times higher compared to the biallelic methylation positions. In addition, we identified that allele-specific methylation sites are more conserved and enriched at the chromatin states corresponding to bivalent promoters and enhancers. Together, these findings suggest that significant impact on methylation can be encoded in the DNA sequence context. In order to elucidate the effect of the SNVs around sites of allele-specific methylation, we applied the Deep Learning model for detection of the cis-regulatory modules and estimated the impact that a genomic variant brings with respect to changes to the regulatory activity of a DNA loci. We revealed higher cis-regulatory impact variants near differentially methylated sites that we further coupled with transcriptomic long-read sequencing results. Our investigation also highlights technical aspects of allele methylation analysis and the impact of sequencing coverage on the accuracy of genomic phasing. In particular, increasing coverage above 30X does not lead to a significant improvement in allele-specific methylation discovery, and only the addition of trio binning information significantly improves phasing. We investigated genomic variation in a single human individual and coupled computational discovery of cis-regulatory modules with allele-specific methylation (ASM) profiling. In this proof-of-concept analysis, we observed that SNPs located near methylated CpG sites on the same haplotype were enriched for sequence features suggestive of high-impact regulatory potential. This finding—derived from one deeply sequenced genome—illustrates how phased genetic and epigenetic data analyses can jointly put forward a hypotheses about the involvement of regulatory protein machinery in shaping allele-specific epigenetic states. Our investigation provides a methodological framework and candidate loci for future studies of genomic imprinting and cis-mediated epigenetic regulation in humans. Full article

The Internet of Medical Things (IoMT) is the latest advancement in the Internet of Things (IoT). Researchers are increasingly drawn to its vast potential applications in secure healthcare systems. The growing use of internet-connected medical device sensors has significantly transformed healthcare, necessitating the development of robust methodologies to assess their integrity. As access to computer networks continues to expand, these sensors have become vulnerable to a wide range of security threats, thereby compromising their integrity. To prevent such lapses, it is essential to understand the complexities of the operational environment and to systematically identify technical vulnerabilities. This paper proposes a unified hesitant fuzzy-based healthcare system for assessing IoMT sensor integrity. The approach integrates the hesitant fuzzy Analytic Network Process (ANP) and the hesitant fuzzy Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). In this study, a hesitant fuzzy ANP is employed to construct a comprehensive network that illustrates the interrelationships among various integrity criteria. This network incorporates expert input and accounts for inherent uncertainties. The research also offers sensitivity analysis and comparative evaluations to show that the suggested method can analyse many medical device sensors. The unified hesitant fuzzy-based healthcare system presented here offers a systematic and valuable tool for informed decision-making in healthcare. It strengthens both the integrity and security of healthcare systems amid the rapidly evolving landscape of medical technology. Healthcare stakeholders and beyond can significantly benefit from adopting this integrated fuzzy-based approach as they navigate the challenges of modern healthcare. Full article

Introduction. Adverse drug events (ADEs) are between the third and sixth most common cause of death worldwide. Biosimulation studies performed using real-world data could generate relevant drug safety information without exposing patients to ADEs. Methods. Iclepertin (BI-425809) was virtually added to the actual drug regimens of n = 4,405,063 individuals. Changes in risk level were estimated for drug-induced long QT syndrome and CYP450 drug interactions. The properties used for iclepertin included: dose of 10 mg (oral) once daily; bioavailability (F) = 71%; Cmax of 222 nM; CYP3A4 weak affinity substrate (partial metabolic clearance of ~80%); IC₅₀ for hERG block of 30 μM. Results. A change in total medication risk score (MRS) was observed (6.3 ± 6.6 to 7.2 ± 6.6) following the addition of iclepertin in ~50% (n = 2,138,247) of the studied population. Among individuals with a change in MRS, ~65% had a 2-unit increase (max 11 units). The number of individuals classified in the High/Severe MRS category increased by 0.33%. The addition of iclepertin to individuals receiving CYP3A4 perpetrator drugs produced a greater change in MRS (+1.5) when compared to individuals not exposed to CYP3A4 perpetrators (+0.8). An additional 0.0032% of the population (n = 139) would be at risk of QT prolongation following the intake of iclepertin. Subset analyses performed in individuals with schizophrenia (targeted indication) demonstrated that these individuals had higher MRS values (13.0 ± 10.3) compared to those without schizophrenia (6.2 ± 6.9). However, the addition of iclepertin did not produce a greater increase in MRS in the schizophrenia population vs. the control population. Our pharmacoeconomic model did not account for any beneficial effects of the drug but the model based on MRS changes predicted a USD 91 yearly increase in medical expenditures (emergency department visits and hospitalizations) per individual (USD 3172 to USD 3263) following the addition of iclepertin. A similar increase was observed in the schizophrenia population following iclepertin addition. Conclusions. The increase in MRS associated with the addition of iclepertin to the drug regimen of a large population was minimal and mostly driven by CYP3A4 interactions. Using this model, interactions can be identified a priori, making risk mitigable and preventable without exposing patients to toxicity. Full article

Background/Objectives: Speech perception is shaped by language experience, with listeners learning to selectively attend to acoustic cues that are informative in their language. This study investigates how language dominance, a proxy for long-term language experience, modulates cue weighting in highly proficient Spanish–English bilinguals’ perception of English lexical stress. Methods: We tested 39 bilinguals with varying dominance profiles and 40 monolingual English speakers in a stress identification task using auditory stimuli that independently manipulated vowel quality, pitch, and duration. Results: Bayesian logistic regression models revealed that, compared to monolinguals, bilinguals relied less on vowel quality and more on pitch and duration, mirroring cue distributions in Spanish versus English. Critically, cue weighting within the bilingual group varied systematically with language dominance: English-dominant bilinguals patterned more like monolingual English listeners, showing increased reliance on vowel quality and decreased reliance on pitch and duration, whereas Spanish-dominant bilinguals retained a cue weighting that was more Spanish-like. Conclusions: These results support experience-based models of speech perception and provide behavioral evidence that bilinguals’ perceptual attention to acoustic cues remains flexible and dynamically responsive to long-term input. These results are in line with a neurobiological account of speech perception in which attentional and representational mechanisms adapt to changes in the input. Full article

Background: Segmenting nerve fibres in histological images is a tricky job because of how much the tissue looks can change. Modern neural network architectures, including U-Net and transformers, demonstrate varying degrees of effectiveness in this area. The aim of this study is to conduct a comparative analysis of the SegFormer, VGG-UNet, and FabE-Net models in terms of segmentation quality and speed. Methods: The training sample consisted of more than 75,000 pairs of images of different tissues (original slice and corresponding mask), scaled from 1024 × 1024 to 224 × 224 pixels to optimise computations. Three neural network architectures were used: the classic VGG-UNet, FabE-Net with attention and global context perception blocks, and the SegFormer transformer model. For an objective assessment of the quality of the models, expert validation was carried out with the participation of four independent pathologists, who evaluated the quality of segmentation according to specified criteria. Quality metrics (precision, recall, F1-score, accuracy) were calculated as averages based on the assessments of all experts, which made it possible to take into account variability in interpretation and increase the reliability of the results. Results: SegFormer achieved stable stabilisation of the loss function faster than the other models—by the 20–30th epoch, compared to 45–60 epochs for VGG-UNet and FabE-Net. Despite taking longer to train per epoch, SegFormer produced the best segmentation quality, with the following metrics: precision 0.84, recall 0.99, F1-score 0.91 and accuracy 0.89. It also annotated a complete histological section in the fastest time. Visual analysis revealed that, compared to other models, which tended to produce incomplete or excessive segmentation, SegFormer more accurately and completely highlights nerve structures. Conclusions: Using attention mechanisms in SegFormer compensates for morphological variability in tissues, resulting in faster and higher-quality segmentation. Image scaling does not impair training quality while significantly accelerating computational processes. These results confirm the potential of SegFormer for practical use in digital pathology, while also highlighting the need for high-precision, immunohistochemistry-informed labelling to improve segmentation accuracy. Full article

This study introduces and evaluates stochastic models to describe Bitcoin price dynamics at different time scales, using daily data from January 2019 to December 2024 and intraday data from 20 January 2025. In the daily analysis, models based on are introduced to capture long memory, paired with both constant-volatility (CONST) and stochastic-volatility specifications via the Cox–Ingersoll–Ross (CIR) process. The novel family of models is based on Generalized Ornstein–Uhlenbeck processes with a fluctuating exponential trend (GOU-FE), which are modified to account for multiplicative fBm noise. Traditional Geometric Brownian Motion processes (GFBM) with either constant or stochastic volatilities are employed as benchmarks for comparative analysis, bringing the total number of evaluated models to four: GFBM-CONST, GFBM-CIR, GOUFE-CONST, and GOUFE-CIR models. Estimation by numerical optimization and evaluation through error metrics, information criteria (AIC, BIC, and EDC), and 95% Expected Shortfall (ES₉₅) indicated better fit for the stochastic-volatility models (GOUFE-CIR and GFBM-CIR) and the lowest tail-risk for GOUFE-CIR, although residual analysis revealed heteroscedasticity and non-normality. For intraday data, Exponential, Weibull, and Generalized Gamma Autoregressive Conditional Duration (ACD) models, with adjustments for intraday patterns, were applied to model the time between transactions. Results showed that the ACD models effectively capture duration clustering, with the Generalized Gamma version exhibiting superior fit according to the Cox–Snell residual-based analysis and other metrics (AIC, BIC, and mean-squared error). Overall, this work advances the modeling of Bitcoin prices by rigorously applying and comparing stochastic frameworks across temporal scales, highlighting the critical roles of long memory, stochastic volatility, and intraday dynamics in understanding the behavior of this digital asset. Full article

Background/Objectives: Understanding the medication prescribing patterns in pediatric primary care is essential for informing policy and clinical practice. In the Kingdom of Saudi Arabia (KSA), and following the 2018 antibiotic restriction policy, limited data exist on the patterns, types, and regimens of antibiotics prescribed during routine family medicine visits for children. This pharmacoepidemiological study aimed to describe the antibiotic prescribing patterns in a university-affiliated model primary healthcare center. Methods: A retrospective chart review was conducted for all the pediatric visits (<14 years) to general family medicine clinics between January and December 2024. Demographic characteristics, visit type, diagnosis, and antibiotic prescription details such as medication class, route, frequency, and duration were extracted from electronic medical records and analyzed descriptively. Results: Among the 2036 pediatric visits, 705 (34.63%) resulted in at least one prescription. Of these, 87 visits (12.34%) included an antibiotic. The most frequently prescribed antibiotic classes were nitroimidazoles (39.29%), penicillins (36.90%), and macrolides (10.71%). Penicillins were typically prescribed for 7 days twice daily as suspensions. Among the non-antibiotic prescriptions, vaccines, nutritional supplements, and analgesics were the most common. Follow-up consultations accounted for 34.09% of all the visits. Conclusions: A lower proportion of antibiotic prescriptions was found when compared to regional and international reports, which may reflect the impact of the antibiotic restriction policy in the country. The findings suggest a shift toward more cautious prescribing in primary care and align with the national efforts to regulate antimicrobial use. Ongoing surveillance of the prescribing trends is essential to evaluate the long-term effectiveness of these measures. Full article

The use of intelligent clinical decision support systems (CDSS) has the potential to improve the accuracy and speed of diagnoses significantly. These systems can analyze a patient’s medical data and generate comprehensive reports that help specialists better understand and evaluate the current clinical scenario. This capability is particularly important when dealing with medical images, as the heavy workload on healthcare professionals can hinder their ability to notice critical biomarkers, which may be difficult to detect with the naked eye due to stress and fatigue. Implementing a CDSS that uses computer vision (CV) techniques can alleviate this challenge. However, one of the main obstacles to the widespread use of CV and intelligent analysis methods in medical diagnostics is the lack of a clear understanding among diagnosticians of how these systems operate. A better understanding of their functioning and of the reliability of the identified biomarkers will enable medical professionals to more effectively address clinical problems. Additionally, it is essential to tailor the training process of machine learning models to medical data, which are often imbalanced due to varying probabilities of disease detection. Neglecting this factor can compromise the quality of the developed CDSS. This article presents the development of a CDSS module focused on diagnosing age-related macular degeneration. Unlike traditional methods that classify diseases or their stages based on optical coherence tomography (OCT) images, the proposed CDSS provides a more sophisticated and accurate analysis of biomarkers detected through a deep neural network. This approach combines interpretative reasoning with highly accurate models, although these models can be complex to describe. To address the issue of class imbalance, an algorithm was developed to optimally select biomarkers, taking into account both their statistical and clinical significance. As a result, the algorithm prioritizes the selection of classes that ensure high model accuracy while maintaining clinically relevant responses generated by the CDSS module. The results indicate that the overall accuracy of staging age-related macular degeneration increased by 63.3% compared with traditional methods of direct stage classification using a similar machine learning model. This improvement suggests that the CDSS module can significantly enhance disease diagnosis, particularly in situations with class imbalance in the original dataset. To improve interpretability, the process of determining the most likely disease stage was organized into two steps. At each step, the diagnostician could visually access information explaining the reasoning behind the intelligent diagnosis, thereby assisting experts in understanding the basis for clinical decision-making. Full article

Background/Objectives: Infertility imposes substantial psychosocial burdens on affected individuals, often resulting in a decline in quality of life comparable to that experienced in chronic diseases. Exploring lifestyle and health awareness-related factors is essential to develop complex, multidisciplinary approaches. This study investigated the associations between the components of 24-h movement behaviour (physical activity, sedentary lifestyle, sleep), health literacy, fertility awareness, and general and infertility-specific quality of life. Additionally, the study assessed whether these factors could predict quality of life outcomes in women living with infertility. Methods: A cross-sectional study was conducted using questionnaire-based data collection in four fertility centres in Hungary. The convenience sample included 361 women aged 18–45 years with a documented infertility diagnosis. Validated questionnaires were used to assess health literacy (BRIEF), fertility awareness (FAS), physical activity (GPAQ-H), sleep quality (AIS), and quality of life (WHOQOL-BREF and FertiQoL). Data analysis included Kolmogorov–Smirnov tests, Spearman correlations, and generalised linear modelling (GLM), with statistical significance set at p < 0.05. Results: Based on the FAS, 77.8% of participants (n = 274) self-reported being adequately informed; however, objective knowledge scores accounted for only 48.5% of the possible total, indicating limited knowledge. Fertility awareness positively correlated with recreational physical activity (ρ = 0.156; p = 0.003). Recreational physical activity showed low but significant positive associations with all quality-of-life dimensions (e.g., psychological well-being: r = 0.177; p ≤ 0.002), whereas sedentary time was negatively associated with psychological well-being (r = −0.109) and social relationships (r = −0.118). Sleep duration correlated positively while sleep quality problems correlated negatively with FertiQoL scores (r = −0.339; p ≤ 0.001). Better sleep quality, lower sedentary time, and higher health literacy were positive predictors of infertility-specific quality of life, whereas higher fertility awareness showed a paradoxical adverse effect. Conclusions: These findings highlight the role of 24-h movement behaviour and health awareness in improving quality of life among women with infertility. The study supports the need for tailored, multi-component lifestyle interventions to promote physical, mental, and psycho-social well-being. Full article

This paper investigates how market expectations regarding profitability mean reversion are reflected in stock prices. We propose a model that infers implicit expectations of future earnings using publicly available share prices based on the assumption that markets efficiently incorporate forward-looking information. The study compares several adjustment models, including the classical partial adjustment framework and a mean reversion model, to identify the most suitable mechanism to capture the dynamics of expected earnings. Special attention is paid to the statistical characteristics of accounting data and ratio-based measures, which influence model performance. Using a dataset covering a twenty-year period, we find that the mean reversion model consistently outperforms partial adjustment models in explaining the behavior of cyclical and random components converging toward a long-term trend. The findings suggest that market prices embed rational expectations of profitability reversion, especially in periods of above average performance. These results align with previous research and provide a robust framework for understanding how earnings expectations are formed and adjusted in financial markets. Full article